441results about "Forest fire alarms" patented technology

The invention discloses a forest defense method and system based on digital twin and intelligent sensors, and the method comprises the steps: collecting and monitoring environment data, and carrying out the integration processing through data fusion; a big data analysis technology is adopted to analyze a potential risk area where a fire occurs, a high-risk area is identified by evaluating a risk level, and a fire spreading trend is predicted; constructing a three-dimensional virtual model of the forest region by adopting a digital twinborn technology; the spreading conditions of the fire at different time and spatial positions are simulated; according to the predicted fire spreading trend, when a potential fire risk is detected, an early warning mechanism is started; automatic generation and screening of the emergency plans are realized, and the emergency plans are generated. The invention provides an emergency protection system integrating the functions of multi-source sensing data fusion, intelligent early warning analysis, dynamic emergency plan generation and the like. In addition, the problem that a traditional emergency plan is static and cannot be dynamically adjusted according to actual conditions is solved.

The invention provides a forest fire early-stage intelligent early warning method based on multi-source remote sensing data, and the method comprises the steps: carrying out the data collection of a target forest through employing an airborne laser radar system and a plurality of sensors, carrying out the data preprocessing, obtaining a three-dimensional forest landscape model and related parameters of the target forest, carrying out the feature extraction, and carrying out the early warning of a forest fire. Obtaining a forest environment key feature library of the target forest, quantitatively evaluating the probability of fire occurrence of the target forest in a specific spatial-temporal scale in the three-dimensional forest landscape model, obtaining a fire risk index, constructing a fire early warning model to solve an optimal early warning strategy, and performing early warning on a target forest region of which the early warning level exceeds a preset threshold value. And automatically forming visual early warning information of the early warning area. Through advanced technologies such as multi-source data fusion, numerical simulation and deep reinforcement learning, a set of complete intelligent forest fire early warning scheme is formed, and reliable data support is provided for forest fire prevention.

The invention provides a forest fire prevention unmanned aerial vehicle patrol early warning system and method based on edge calculation, and relates to the technical field of unmanned aerial vehicle patrol early warning. The patrol early warning system comprises an unmanned aerial vehicle platform, a data acquisition module, a data processing and analysis module, a communication and early warning module and a ground control and scheduling platform. According to the invention, calculation and data storage are close to a data source through edge calculation, the data collected by the unmanned aerial vehicle is processed in real time at an edge calculation node without being transmitted to a remote cloud and returning a result, the data processing and response time is greatly shortened, fire hazards can be found in seconds or even shorter time, and early warning can be given out. And the system can analyze data in real time in the flight process of the unmanned aerial vehicle, provides real-time decision support for a ground control center, and can quickly formulate a fire-fighting scheme according to information such as fire behavior and wind direction when a fire occurs, so that the fire-fighting efficiency is improved.

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention provides a forest fire prevention early warning method and system based on a multi-source sensor, and the method comprises the steps: firstly obtaining a multi-source sensing monitoring data set of a target forest region, including data subsets of temperature, humidity, smoke concentration, visible light images and the like, and collecting each subset by at least two heterogeneous sensor nodes; performing space-time alignment processing on the multi-source sensing monitoring data set to generate a space-time distribution characteristic set containing various characteristics such as temperature gradient, calling a dynamic anomaly detection model to compare fire danger indexes, generating a fire danger grade quantitative score and an anomaly fluctuation index set, and performing data processing on the fire danger grade quantitative score and the anomaly fluctuation index set; the method comprises the steps of obtaining a fire hazard grade quantitative score, matching a multi-level early warning strategy based on the fire hazard grade quantitative score and a preset early warning threshold interval, generating a dynamic fire hazard early warning instruction, triggering fire extinguishing resource scheduling according to the dynamic fire hazard early warning instruction, generating a forest region escape path optimization strategy in combination with an abnormal fluctuation index, and achieving efficient and accurate forest fire prevention early warning and coping.

An intelligent automated community wildfire detection and suppression control system, an ember trap detector, and a home perimeter ember sensor automated control and activation system are disclosed. The intelligent automated community wildfire detection and suppression control system, the ember trap detector, and the home perimeter ember sensor automated control and activation system uses unique low-cost passive and mesh-networked sensors and mechanical devices that relay data to central control systems for real-time artificial intelligence predictive software control. Wildfire control and suppression systems use this smart data for precision passive wildfire control of large areas.

The invention discloses a fire risk accurate prediction method and system based on a multi-source data fusion knowledge graph, and belongs to the technical field of machine learning, and the method comprises the following steps: accessing a multi-source heterogeneous data set, integrating sensor data, geographic information, historical data and external data, and obtaining a multi-source heterogeneous data set; a full-size forest three-dimensional model is constructed by means of FDS and SolidWorks, combustion simulation is carried out, environmental parameters are set carefully to ensure that the result is accurate, and a foundation is laid for construction of a multi-source heterogeneous data set; the method comprises the steps of data acquisition, data preprocessing, noise data cleaning, space-time alignment and feature engineering, multi-source heterogeneous data acquisition and preprocessing are carried out, and forest fire related data covering structured, semi-structured and non-structured types are acquired from a multi-source heterogeneous data set. The accuracy and timeliness of fire early warning are remarkably improved, effective fusion of forest fire multi-source heterogeneous data can be achieved, and transparency and traceability in the data fusion process are ensured.

The invention provides a forest fire spreading simulation method and system based on an unmanned aerial vehicle LiDAR technology, and the method comprises the steps: collecting forest region data based on the unmanned aerial vehicle LiDAR technology, a camera and a sensor; wherein the forest area data comprises LiDAR point cloud data, thermal infrared data, image data and meteorological data; based on the forest region data, performing forest region feature extraction to obtain feature parameters; based on the characteristic parameters, constructing a fire spread dynamic model through a fire spread dynamics reinforcement learning framework and a vertical-horizontal fire spread coupling algorithm; based on the fire spreading dynamic model, fire spreading simulation and prediction are carried out, dynamic early warning is carried out based on simulation and prediction results, and a fire fighting and fire prevention decision is obtained. The technical scheme of the invention has important significance for protecting forest resources and maintaining ecological balance.

The invention discloses a forest fire dynamic monitoring method and system based on multi-source data fusion, and relates to the technical field of fire dynamic monitoring, and the method comprises the steps: carrying out the calculation of the fire occurrence probability of a forest region, and outputting a dynamic risk thermodynamic diagram; non-uniform deployment of integrated sensing units is carried out to form a ground fire sensing network; large-range abnormal hot spot distribution is obtained; performing abnormal hot spot cooperative verification, and outputting a real-time fire risk alarm; matching a high-credibility unmanned aerial vehicle according to the fire risk alarm to execute real-time fire image acquisition; fire spreading modeling is carried out in combination with real-time meteorological data, and a fire spreading trend prediction result is obtained; and executing high-response fire extinguishing management and control of the forest area. According to the invention, the technical problem of low fire extinguishing management and control efficiency caused by inaccurate forest fire monitoring risk in the prior art is solved, and the technical effects of realizing dynamic monitoring and efficient management and control of forest fire and improving the efficiency and timeliness of fire extinguishing management and control are achieved.

The invention discloses a multi-stage power transmission line forest fire identification early warning analysis method and system, and relates to the field of power transmission line forest fire early warning. Power transmission line region division information is acquired, a target key patrol region is extracted, and a forest fire risk level of the target key patrol region is calculated; acquiring a forest fire alarm preset threshold value of the target key patrol area; according to the forest fire risk level, determining an adjustment amplitude of a forest fire alarm preset threshold, and obtaining a parameter interval of the forest fire alarm preset threshold; environment information of the target key patrol area is identified, a specific value in a parameter interval is determined according to the environment information, and a preliminary threshold value is obtained; and identifying the forest fire development situation of the target key patrol area, determining an adjustment coefficient according to the forest fire development situation, and endowing the adjustment coefficient with a preliminary threshold to obtain an alarm threshold. Multi-stage identification early warning is realized by adopting a dynamic threshold value mode, real-time adjustment is performed according to environment change, false alarm and missing alarm are greatly reduced, and early warning accuracy is improved.

The invention relates to the field of power transmission line forest fire monitoring, and discloses a power transmission line multi-mode fusion intelligent forest fire monitoring method which is used for constructing an all-dimensional power transmission line forest fire prevention monitoring system and remarkably improving the intelligent level of power transmission line forest fire risk prevention and control. Comprising the steps of reasoning and identifying a smoke and fire target for a fused feature set by utilizing an improved YOLOv8 algorithm model, realizing accurate positioning of a fire point by adopting a haze penetration thermal imaging technology and a smoke cooperative monitoring technology in combination with spatial positioning, and establishing a temperature and humidity and forest fire occurrence probability correlation model based on historical data. According to the real-time temperature and humidity parameter dynamic adjustment algorithm model, the confidence threshold and the sensor acquisition frequency are detected, a dynamic threshold control method is adopted, an early warning threshold is adaptively adjusted according to parameters such as environment illumination, a final early warning result is output after cross validation of a multi-modal data fusion strategy, and the safety of the power transmission line is effectively guaranteed.

The invention provides a control method of a forest fire prevention early warning system, which comprises the following steps: aiming at different types of litters, carrying out a combustion characteristic test, and measuring ignition points, heat values and combustion rate parameters of the litters to obtain combustion characteristic data of the different types of litters; on the basis of grading of the litter layer fire danger, analyzing spatial distribution characteristics of the litter layer fire danger, identifying a fire danger high-incidence area, and representing spatial aggregation and relevance of the litter layer fire danger by adopting a spatial autocorrelation analysis method; analyzing dynamic change characteristics of the litter layer fire danger through a litter layer fire danger assessment result, summarizing a seasonal change rule and an annual change trend of the litter layer fire danger by adopting a time sequence analysis method, and identifying a fire danger high-incidence time period; a geographical detector method is adopted to quantitatively analyze the influence degree of terrain, weather and vegetation on the litter layer fire danger, the dominant key factors are identified, and the key factors influencing the litter layer fire danger are explored.

The invention belongs to the technical field of safety early warning, and discloses a forest fire suppression remote command system based on an artificial intelligence meteorological big model and a digital twinborn technology. The laser radar monitoring module collects fire scene basic information and transmits the fire scene basic information to the artificial intelligence meteorological large model prediction module for trend prediction. A prediction result and monitoring data are fused through the digital twinborn situation display module and then fire scene situation information is displayed. A commander makes a decision based on the three-dimensional visual fire scene environment, the command decision and emergency response module generates a hierarchical response and combat scheme, and the personnel, material and air-ground collaborative scheduling module executes specific tasks according to the hierarchical response and combat scheme. The method has the advantages that the fire scene information can be acquired in real time and completely; the prediction capability is obviously improved and dynamically updated; multi-source data deep fusion and digital twinning dynamic display are realized; a complete hierarchical response and command closed loop is formed; and cross-region and cross-department resource scheduling is more efficient.

The invention discloses a forestry fireproof early warning method and system based on the Internet of Things, and relates to the technical field of forestry monitoring, prevention and control, a heterogeneous sensing network is constructed through millimeter wave radar and multispectral imaging, and omnibearing monitoring in a complex environment is realized by combining terrain shielding coefficient Dmxs and signal coverage intensity Xgql analysis. The thermal radiation intensity Rsqd, the combustible drying index Gzzs and the airflow disturbance coefficient Qlys are fused to improve the hidden fire source identification precision; a LoRa relay and a Mesh network are adopted to ensure communication stability of a weak signal area, data transmission is optimized through edge calculation, and high-risk area resources are preferentially scheduled based on hierarchical response of a joint early warning index Lyzb; the risk assessment module calculates a fire danger index Hxzs in real time to predict fire spreading, the intelligent decision-making module comprehensively assesses hidden fire source probability Yhgl and a positioning error Wcsp to generate an early warning strategy, and the emergency linkage module automatically executes hierarchical response to form an intelligent monitoring-early warning-disposal closed-loop management system.

The invention provides a forest disaster detection method and device, and belongs to the field of forest disaster monitoring, and the method comprises the steps: collecting an infrared image, a stereoscopic vision image and positioning data of a target forest region in real time; an ASSA module is introduced in the feature extraction stage of the YOLOv11, and an infrared image is input into the improved YOLOv11 for flame target recognition; recognizing the stereoscopic vision image through a smoke and fire recognition algorithm, and detecting flame and smoke targets; determining a fire detection result by combining the identification results of the flame target and the smoke target; under the condition that the fire disaster is determined to occur, an HITNet algorithm is adopted to carry out ignition point positioning through a stereoscopic vision image, and the geographic coordinates of a fire disaster point are determined based on positioning data; and sending alarm information based on the fire point geographic coordinates. Therefore, the capability of extracting the flame heat radiation characteristics of the tiny fire points is effectively enhanced, and the detection result of the forest fire is improved.

The invention discloses an unmanned aerial vehicle-mounted GNSSINS visual combination fire detection method, system and device and a medium, belongs to the technical field of unmanned aerial vehicles, and aims to solve the technical problem of how to improve the positioning precision and fire recognition capability of an unmanned aerial vehicle in a complex environment. Setting a dynamic temperature threshold value, and calibrating the threshold value in real time in combination with the environment temperature; identifying a fire source diffusion trend by adopting a temperature gradient analysis method, and excluding isolated high-temperature points; an image is collected through an RGB camera and converted into an HSV color space, flame features are extracted, and the flame shape is detected in real time through a YOLOv5 model; detecting a smoke area based on a U-Net segmentation network, outputting a binary mask, carrying out space-time alignment on the binary mask and the wind speed and wind direction of the meteorological data, and predicting the fire spreading direction; thermal imaging, visible light and smoke detection results are fused through the D-S evidence theory, and when the confidence coefficients of the thermal imaging, visible light and smoke detection results are all larger than 80%, it is judged that a fire exists.

The invention discloses a power transmission line forest fire early warning method based on multi-modal image fusion, and aims to solve the problems that when an existing forest fire early warning method is in butt joint with the actual operation condition of a power transmission line, multi-source information complementation is difficult to consider at the same time, false detection and missing detection are often caused by environmental influence, and the early warning timeliness sometimes cannot meet the real-time requirement of power grid operation. According to the method, a power transmission line forest fire early warning model based on multi-modal image fusion is constructed, and the model comprises preprocessing, multi-scale feature extraction, fire point detection and positioning, risk level definition and early warning level division. And training the model according to the visible light image, the infrared image and the radar image at each moment in the process from the beginning to the end of the mountain fire combustion. The invention belongs to the technical field of power transmission line forest fire early warning.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention discloses a forest fire audio recognition method based on a BP neural network, a medium and a product in the technical field of forest fire recognition. The method comprises the following steps: acquiring forest fire audio data to be detected; extracting a Mel-frequency cepstrum coefficient (MFCC) based on the frequency domain features of the forest fire audio data to be detected; inputting the Mel-frequency cepstral coefficient MFCC into a pre-trained forest fire audio recognition model based on a BP neural network, and outputting a forest fire category; wherein the forest fire category comprises a forest fire and a non-forest fire. Compared with a traditional visual monitoring means, the method can cover a wider forest area, in the same monitoring range, in addition, model training based on the BP neural network can efficiently process a large amount of audio data and rapidly output an identification result, the efficiency and accuracy of forest fire identification are further improved, and the method is suitable for popularization and application. The problems of limited coverage range, high cost and low accuracy in the existing forest fire identification technology are solved.

The invention relates to the technical field of disaster prevention and reduction of power systems, in particular to a power transmission line forest fire spreading range prediction method and system based on multi-factor fusion, and the method comprises the steps: collecting multi-source data in real time, and building a fusion database through time-space alignment processing; extracting fire scene features by adopting an image processing technology, and calculating a live line distance and vegetation combustion parameters by combining laser radar data; constructing a prediction model, and dynamically correcting fire spreading parameters; and simulating a spreading path based on the three-dimensional model and topographic data, evaluating the influence degree of the fire behavior on the power transmission line, and generating an early warning strategy. The problems of smog shielding monitoring failure, insufficient night prediction precision and the like caused by dependence of a single data source are effectively solved, the forest fire early warning capability in a complex environment is remarkably improved, and high-precision and real-time technical support is provided for disaster prevention and reduction of a power system.

The invention provides a radar monitoring intelligent early warning method and system suitable for forest fire prevention, and relates to the technical field of forest fire prevention. The method comprises the steps of establishing a three-dimensional map, and dividing a to-be-detected area; acquiring forest fire factor data; a forest fire risk assessment model based on the convolutional neural network is constructed, hyper-parameters of the model are optimized by using a Hereum algorithm, and risk grading is performed on the to-be-detected area; fixed radar monitoring is carried out on the high-risk-level area; generating a mobile monitoring path of the low-risk level area; and constructing a prediction model to carry out fire early warning. According to the method, the detection area is finely divided, the high-risk-level area is continuously monitored by the fixed monitoring radar, and the low-risk area is regularly patrolled by the mobile radar, so that the deployment number of the fixed radar is greatly reduced, and the forest fire early warning cost is reduced. And meanwhile, comprehensive monitoring of the whole target forest is realized, and the limitation of a single monitoring means is made up.

The invention relates to a method for suppressing and / or extinguishing a wildfire using a wildfire extinguishing station, having the steps of receiving information, generating a control command, transmitting the control command or information, and carrying out the wildfire suppression process. The invention additionally relates to a wildfire suppression system comprising a network device, a server unit, a gateway, a first terminal, said first terminal having a sensor unit, and a second terminal, said second terminal having a wildfire suppression element.

The invention relates to a method for forest fire early detection having the steps of implementing machine learning data (ML data) for the detection of forest fires in a forest fire early detection system, recording measurement data by a terminal device of the forest fire early detection system and determining result data by applying the ML data to the measurement data recorded by the terminal device, with the ML data being implemented in the terminal device, as well as a forest fire early detection system with a LoRaWAN network.

The invention relates to the technical field of forest fire early warning, in particular to a meteorological-assisted forest fire efficient early warning and scheduling system which comprises a meteorological data analysis module, a rescue resource scheduling module, a real-time path planning module and a fire behavior mode prediction module. According to the invention, through the application of real-time monitoring and data analysis, the response speed and accuracy of forest fire early warning are greatly improved, the real-time measurement and analysis of meteorological variables enable fire early warning not to only depend on a traditional probability model, but can dynamically adjust the early warning threshold based on real-time data, so that early warning is more timely and highly targeted, and the early warning efficiency is improved. The problem of data updating delay is solved, accurate risk assessment is provided, instant fire alarm issuing and real-time optimization of a rescue path are ensured, rescue resources can be ensured to quickly respond through the shortest path, the success rate and efficiency of rescue actions are directly influenced, and a fire behavior prediction model utilizes continuous data training to improve the rescue efficiency. And the adaptability and prediction capability to the fire change are further enhanced.

The invention provides an unmanned aerial vehicle deployment method, device, equipment and medium in a mountain dense forest emergency rescue scene, and the method comprises the steps: building a non-line-of-sight avoidance virtual force model, and solving the total resultant force of virtual forces borne by an unmanned aerial vehicle according to the non-line-of-sight avoidance virtual force model; constructing a particle swarm optimization model by taking each unmanned aerial vehicle deployment scheme as a particle, and optimizing the speed and position of the particle by combining the total resultant force of the virtual force of each unmanned aerial vehicle so as to dynamically adjust the deployment position of the unmanned aerial vehicle group; and iteratively solving the particle swarm optimization model until a preset positioning precision requirement is met or a maximum iteration number is reached, and outputting an optimal deployment position. According to the invention, the number of non-line-of-sight links can be effectively reduced by dynamically adjusting the deployment position of the unmanned aerial vehicle group; and a particle swarm optimization model is combined to solve the problems that an artificial potential energy field method has high dependence on the initial deployment position of the unmanned aerial vehicle and the initial position needs to be adjusted for multiple times, and the positioning precision is improved.

The invention discloses a fire behavior identification method, system and device suitable for power grid inspection and a medium, and belongs to the technical field of mountain fire monitoring, and the method comprises the steps: obtaining multi-mode video data of a to-be-monitored region, and obtaining a synchronized multi-mode video frame according to the multi-mode video data; determining a baseline model of preliminary training convergence based on the synchronized multi-modal video frame and a preset forest fire recognition model; obtaining an optimized baseline model by obtaining unlabeled video data and combining the baseline model; and performing fire behavior identification based on the optimized baseline model and the synchronized multi-mode video frame to obtain a fire behavior identification result. The method has the beneficial effects that the accuracy and reliability of fire identification are improved, continuous and stable detection under the actual background condition across day and night is realized, and accurate and timely fire early warning guarantee is provided for power grid inspection and forest fire prevention.

Embodiments of the Invention provide real-time portable, deployable data acquisition units and monitoring consoles that can be used in combination with radio communication technology to provide for monitoring of wildfires and local weather conditions to aid in fighting wildfires.

A system and method for detecting in real-time High-Risk-Lightning (HRL) strikes and sending out alerts to responsible personnel to allow for earlier responses to lightning caused fire ignitions, and powerline damages, to help maintain and / or reduce the chance of spread by the wildfire and reduce the amount of powerline downtime. The system and method allow for HRL events and fire ignitions / powerline damage points to be detected preferably within seconds. The system and method can use a network of detectors, data from environmental satellites and / or other environmental data sources, powerline data, and novel AI / algorithms for signal processing for relatively quickly locating fire ignition spots and powerline damage points. Thus, the system and method provide for actionable wildfire intelligence and powerline damage potential in real-time and for relatively quickly and accurately sending out alerts when an HRL event has been determined. Cameras and drones can be used to provide real-time visualization at the location of the HRL event to verify or monitor any fire ignition or smoldering at the area of the HRL event, and to check the potential damage that the HRL strike has caused to the powerline. In one embodiment, all of the main components for the detection system can be located together out in the field and preferably connected to a pole or other vertical or substantially vertically oriented object. The system can also include a concentric antenna configuration for the high frequency antenna and low frequency antenna. The HRL data, including location coordinates, can be used for other related third party uses, such as inputs for a fire spread modeling software to yield a more accurate fire spread model, as well as for producing fire related maps, such as Community Wildfire Protection Plan maps that are relied on by the public.

The invention discloses a multi-mode forest fire identification system and method based on an unmanned aerial vehicle, and belongs to the technical field of fireproof monitoring, and the system comprises the unmanned aerial vehicle, a monitoring center, a user terminal, a path planning unit and a wireless communication unit. Wherein the unmanned aerial vehicle is loaded with a high-definition camera, a position sensor, an infrared sensor and an edge computing server; a multi-modal fire recognition algorithm model runs on the edge server, and the multi-modal fire recognition algorithm model comprises an image recognition algorithm, an infrared data analysis algorithm, a data fusion module and a decision support module; and the processor is used for processing the visible light video data acquired by the high-definition camera and the infrared data acquired by the infrared sensor in real time and outputting an analysis result. According to the invention, the early warning speed and accuracy of a fire disaster can be obviously improved, remote real-time monitoring is realized, false alarms can be reduced, and rapid and accurate fire source positioning is realized.

The invention provides a forest fire monitoring method, device and equipment, and relates to the technical field of computers. In the application, firstly, based on environment acquisition information corresponding to each sub-region, a region relationship network is constructed; secondly, based on the environment semantic vectors of the environment collection information corresponding to other sub-regions, according to the incidence relation represented by the region relation network, semantic aggregation is carried out on the environment semantic vectors of the environment collection information corresponding to the target sub-region, and an aggregated environment semantic vector is formed; then, based on an image semantic vector corresponding to the forest remote sensing image, performing semantic enhancement on the aggregated environment semantic vector to form an enhanced environment semantic vector; and finally, performing semantic analysis based on the enhanced environment semantic vector, and outputting target fire monitoring data for reflecting the probability of fire occurrence. Based on the above content, the problem that the reliability of forest fire monitoring is relatively low in the prior art can be improved.